Recent practices in open-domain question answering (QA) can be traced to the Text Retrieval Conferences (TRECs) in the late 1990s. [1]} defines the task of textual open-domain question answering as using a small text snippet, usually an excerpt from a document as part of a large collection that is being utilized in the process, such as web pages [2]}. In the last decade, the focus on open-domain question answering has shifted to the re-ranking stage, where answer identification from candidate documents is performed using learning strategies based on richer and better language understanding models [3]}, [4]}, [5]}, [6]}, [7]}. Our approach also tries to propose models that improve the re-ranking part of the QA pipeline. Specifically, we are different from alternate approaches that perform end-to-end question answering that requires some type of term-based retrieval technique to restrict the input text under consideration [8]}, [9]}, [10]}.
